Predicting respiratory distress

ABSTRACT

A system, methods, and computer-readable media are provided for the automatic identification of patients having an elevated near-term risk of pulmonary function deterioration or respiratory distress. Embodiments of the invention are directed to event prediction, risk stratification, and optimization of the assessment, communication, and decision-making to prevent respiratory events in humans, and in one embodiment take the form of a platform for wearable, mobile, untethered monitoring devices with embedded decision support. Respiratory information is obtained over one or a plurality of previous time intervals, to classify a likelihood of events leading to an acute respiratory decompensation event within a future time interval. In an embodiment, the risk prediction is based a plurality of nonlinearity measures of capnometry information over the previous time interval(s), and the risk for an acute respiratory decompensation event determined using an ensemble model predictor on the nonlinearity measures.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/979,855, titled “PREDICTING RESPIRATORY DISTRESS,” filed on Apr. 15,2014; which is hereby expressly incorporated by reference in itsentirety.

INTRODUCTION

Asthma and chronic obstructive pulmonary diseases, such as COPD,bronchitis, bronchiectasis, emphysema, and the like, are long-termconditions that affect the airways. Chronic lower respiratory disease,primarily COPD and asthma, was the third leading cause of mortality inthe U.S. in 2011, and 15 million Americans report that they have beendiagnosed with COPD. Classic symptoms include dyspnea, tightness in thechest, wheezing, and coughing. The objective of management is for peopleto be symptom-free and able to lead an active life. This is achieved bytreatment, preferably personalized to the particular needs andcircumstances of each individual patient, and by educating each patientas to the exposures or events that trigger acute the respiratoryexacerbations they experience, so as to enable the patient to avoidthese triggers as much as possible.

The causes of obstructive lung diseases are diverse, including geneticand environmental causes, and cure is not generally possible, althoughresolution can sometimes be achieved in occupational asthma.Occupational factors account for about 10% of cases of newly incidentasthma in adults of working age. The goals of care in chronicobstructive lung diseases are to control the condition, so as to reducedisease progression; to reduce the severity of disease and the morbiditythat is associated with concomitant conditions; and to minimize episodesof acute decompensation and respiratory distress or respiratory failure.

For persons with asthma or COPD, acute, unplanned respiratory functiondeterioration resulting in the need for emergency department orin-patient hospital care is a frequent problem worldwide. In the U.S. in2011, AHRQ records indicate that 1.1 million in-patient admissions, 2.9million emergency department (ED) episodes, and 8.9 million doctoroffice visits occurred for individuals with a principal diagnosis in theICD-9 code 491.0-494.9 range, resulting in $26 billion of annualin-patient charges, $4.3 billion ED charges, and more than $56 billionin annual direct-care health expenses overall. The weighted averagemortality rate was 0.95% for in-patients admitted to hospital withprincipal diagnosis in this range of ICD-9 codes during 2011. Thelikelihood of acute pulmonary insufficiency increases substantially withadvancing age and comorbid health conditions. The average medicalcharges associated with an in-patient episode resulting from an acuteexacerbation of a principal condition in the ICD-9 code 491.0-494.9range presently exceed $26,000, and the total acute-care cost of acuterespiratory events in the context of chronic obstructive lung disease isexpected to reach $28 billion in 2020.

Critical asthma syndrome comprises life-threatening asthma and statusasthmaticus. Life-threatening asthma exacerbations are defined asdyspnea so severe that the patient is unable to speak, markedlydecreased peak expiratory flow (PEF)<25% of a patient's personal best,and failure to respond to bronchodilator medication and intravenouscorticosteroids. Critical asthma syndrome cases require emergency care,and most cases require hospitalization, usually in an intensive careunit. Among asthmatics, those with the critical asthma syndrome are themost difficult to manage.

Procedural prevention programs attempting to reduce the incidence ofacute respiratory distress in the setting of COPD or asthma have to-datehad mixed effectiveness, in part because the preventive measures addressonly a subset of the antecedent factors that lead to these events and inpart because they place a portion of the burden of event-prevention uponpersonnel other than the person who is at risk.

SUMMARY

Systems, methods and computer-readable media are provided for theautomatic identification of patients having an elevated near-term riskof pulmonary function deterioration or respiratory distress. Embodimentsof the invention are directed to event prediction, risk stratification,and optimization of the assessment, communication, and decision-makingto prevent respiratory events in humans, and in one embodiment take theform of a platform for wearable, mobile, untethered monitoring deviceswith embedded decision support. Thus, the aim of embodiments of thepresent invention relates to automatically identifying persons who areat risk for acute respiratory deterioration through the use of aninexpensive, noninvasive, portable electronic device and sensorsequipped with signal-processing software and statistical predictivealgorithms that calculate nonlinearity properties of capnometrytimeseries acquired by the device. The measurements and predictivealgorithms provide for use in general acute-care and chronic-care venuesand afford a degree of robustness against variations in individualanatomy and sensor placement. In some embodiments, the present inventionprovides a leading indicator of near-term future abnormalities,proactively notifying clinicians caring for the user and providing thecare providers with sufficient advance notice (such as hours, days, orweeks in advance) to enable effective preventive maneuvers to beundertaken. In one exemplary embodiment, the device is integrated withcase-management software and electronic health record decision-supportsystems, including occupational health, health insurance, and disabilityassessment decision-support systems.

In one aspect, a method is provided for automatically predicting anacute respiratory decompensation event that is likely to result in theneed for acute medical attention. The method includes the step obtainingcapnometry signals representative of capnometry of an individual;detecting the presence of abnormal timeseries nonlinearity properties ofcapnometry measurements in said signals. The method also includes thesteps of determining, utilizing an objective function, a capnometrynonlinearity score (CNS) from the signals based on one or a plurality ofprevious time intervals. In an embodiment, the method also includes anddetermining a difference between a plurality of metrics comprising theCNS and a reference value to classify the likelihood of events leadingto pulmonary function decompensation within a future time interval,wherein a difference from the reference value is indicative of increasedrisk for acute respiratory distress, wherein the reference value may bedetermined based on clinical parameters associated with the individualsuch age, mobility, pulmonary function decompensation, or other clinicalparameters associated with the individual. In one embodiment, the methodfurther includes providing a notification when an increased risk foracute respiratory distress is determined. In some embodiments, thisnotification may be communicated to a health care provider and/or may becommunicated to the individual by means of an audible alarm, textmessage, or phone call or other electronic notification.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 depicts aspects of an operating environment suitable forpracticing an embodiment of the invention;

FIGS. 2A-2B depict aspects of an operating environment suitable forpracticing an embodiment of the invention;

FIG. 3A depicts a flow diagram of a method for automatically predictingdyspnea and acute respiratory distress risk in an individual with anobstructive pulmonary condition, where the distress is of a type andseverity as are likely to result in unplanned requirement for attentionin a hospital or emergency department, in accordance with embodiments ofthe invention;

FIG. 3B depicts a flow diagram of a method for automatically predictingelevated risk of respiratory distress, in accordance with an embodimentof the invention;

FIG. 4 depicts a portion of waveform representing a mixed-expiratorymainstream-sampled feCO₂ timeseries;

FIGS. 5A and 5B depicts aspects of expiratory timeseries data andcomputations, in accordance with embodiments of the invention;

FIG. 6A depicts a Receiver Operating Characteristic (ROC) curverepresenting the accuracy and discriminating classificatory capacity ofthe invention in a cohort of 16 subjects, and tables showing thestatistical performance of one embodiment of the invention in theinitial cohort studied;

FIG. 6B depicts an example of a computer program for determining the ROCcurve provided in FIG. 6A, in accordance with embodiments of theinvention; and

FIGS. 7A-7C illustratively provide an example embodiment of a computerprogram routine for determining nonlinearity measures ofcapnometry-information timeseries and performing an ensemble ofmeasures, in accordance with an embodiment of the invention.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

As one skilled in the art will appreciate, embodiments of our inventionmay be embodied as, among other things: a method, system, or set ofinstructions embodied on one or more computer readable media.Accordingly, the embodiments may take the form of a hardware embodiment,a software embodiment, or an embodiment combining software and hardware.In one embodiment, the invention takes the form of a computer-programproduct that includes computer-usable instructions embodied on one ormore computer readable media.

Computer-readable media include both volatile and nonvolatile media,removable and nonremovable media, and contemplate media readable by adatabase, a switch, and various other network devices. By way ofexample, and not limitation, computer-readable media comprise mediaimplemented in any method or technology for storing information,including computer-storage media and communications media. Examples ofstored information include computer-useable instructions, datastructures, program modules, and other data representations. Computerstorage media examples include, but are not limited toinformation-delivery media, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile discs (DVD), holographicmedia or other optical disc storage, magnetic cassettes, magnetic tape,magnetic disk storage, other magnetic storage devices, and othercomputer hardware or storage devices. These technologies can store datamomentarily, temporarily, or permanently.

At a high level, embodiments of the invention pertain to monitoringhuman patients and quantitatively predicting whether or not an elevatedrisk of acute deterioration of pulmonary function prevails within anacute time interval of up to several weeks subsequent to computing theprediction and, if such is the case, informing the care providerclinicians' decisions and interventions to mitigate the risk and preventthe occurrence of acute, unplanned emergency department episodes orhospital admissions and concomitant morbidity in such patients.

More specifically, embodiments of the invention provide systems andmethods for the automatic identification of patients having an elevatednear-term risk of pulmonary function deterioration or respiratorydistress. In particular, embodiments of the invention are directed toevent prediction, risk stratification, and optimization of theassessment, communication, and decision-making to prevent respiratoryevents in humans, and in one embodiment take the form of a platform forwearable, mobile, untethered monitoring devices with embedded decisionsupport. Accordingly, embodiments of the invention facilitateautomatically identifying persons who are at risk for acute respiratorydeterioration through the use of an inexpensive, noninvasive, portableelectronic device and sensors equipped with signal-processing softwareand statistical predictive algorithms that calculate nonlinearityproperties of capnometry timeseries acquired by the device.

As described above, existing procedural prevention programs attemptingto reduce the incidence of acute respiratory distress in the setting ofCOPD or asthma have to-date had mixed effectiveness, in part because thepreventive measures address only a subset of the antecedent factors thatlead to these events and in part because they place a portion of theburden of event-prevention upon personnel other than the person who isat risk. In that connection, one motivation for some embodiments of theinvention is that, were people with obstructive lung disease whosenear-term risks of acute pulmonary exacerbations are recently elevatedor increasing notified of that risk or risk-increase, many such patientswould respond to the notifications by proactively self-initiatingpreventive measures, including temporarily adjusting their dailyactivities and exposures to respiratory irritants and contactingcaregivers for help. Psychologically, this is far preferable to patientpassivity and reactive responses by caregivers, insofar as persons atrisk of respiratory insults not only fear such events; they also fearloss of independence and freedom.

As further noted above, acute decompensation of respiratory function isassociated with common chronic diseases that obstruct the airways, suchas asthma and chronic obstructive lung disease (COPD). But it is alsoassociated with less common obstructive conditions such as cysticfibrosis. Acute exacerbation of obstructive respiratory symptoms mayalso be intercurrent with, or secondary to, Alzheimer's or other formsof dementia where obstruction is due to generalized weakness,inattention to self-care, and poor bronchial toilet; scoliosis or otherskeletal conditions that compromise the mechanics of breathing; orParkinson's disease or ALS or other neurologic conditions accompanied bydifficulty clearing bronchial secretions. However, while the absence ofsuch conditions does reduce respiratory risk to a degree, it does notexclude the possibility of acute respiratory distress. It is for thisreason that so much effort has been expended over the past 30 years ondeveloping diagnostic tests, such as FEV1/FVC ratio, exhaled nitricoxide measurement, and other metrics.

Mechanisms and types of pulmonary function decompensation have been thesubject of several studies. Seasonal allergen and other environmentalfactors account for a high percentage of respiratory events andsubsequent services utilization in community-dwelling older adults aswell as children. However, existing oximetric, capnometric, nitricoximetric, spirometric, and other prior art methods, while able todetermine the overall severity of respiratory illness, are relativelyinsensitive and inaccurate with regard to predicting future acuterespiratory distress events.

Further, anxiety or other emotional triggers play a significant role inthe onset of acute respiratory distress in individuals with asthma orCOPD. However, other approaches to diagnostic and monitoring spirometryand nitric oximetry methods typically entail short (1- to 15-second)tests that encompass too small a time interval to have a realisticchance of capturing the influence of fluctuating emotions ortime-varying adrenergic and cholinergic neuroendocrine phenomena thatcan precipitate acute respiratory distress.

Acute exacerbations of asthma and COPD are frequent causes ofabsenteeism among persons of employment age. In 2008, asthma caused 14.2million missed days of work. Additionally, respiratory conditions arefrequently the subject of occupational medicine and health insurancedecision-making. Determination of bona fide disability in an accurateand fair manner has major public health importance and financialsignificance for employers and insurers. In the 4-year interval betweenJanuary 2009 and December 2012, 5.9 million people in the U.S. wereadded to the Social Security Disability program. This contrasts with on2.5 million jobs that were added during the same 4 years. Approximately4% (470 thousand) of the 10.9 million people on U.S. federal disabilityhave claims based on chronic respiratory ailments.

Besides accurate assessment of true functional impairments, accurate andreliable ascertainment of malingering and attempts to defraud healthplans, employers, and insurers by persons' factiously simulatingrespiratory impairment is of great economic importance. Medicalmalingering for the pursuit of external, financial gain is anincreasingly significant social and economic problem in recent years.Persons who wish to be classified as disabled to be placed on the publicdole routinely attempt to fabricate a dossier of evidence of healthproblems. Often, they present themselves at hospital emergencydepartments in serial weekly or monthly episodes to accomplish this,co-opting health care workers in the construction of the dossier. Acuterespiratory ailments are a favorite among malingerers. Compared tomalingering involving psychiatric or pain symptoms, covertly injectingsaliva to produce sepsis, ingesting rat poison or warfarin to produce ableeding disorder, ingesting OTC laxatives to cause diarrhea, orfeigning seizures or syncope, the pattern of paroxysmal onset and rapidabatement of respiratory signs and symptoms is ideal for perpetrators ofmedical fraud. In respiratory malingering, the brief play-acting doesnot involve significant personal injury or health risk. The situationacted-out does not entail that the actor will undergo painful, invasive,inconvenient, time-consuming examinations, unpleasant treatments, orsocial stigmatization. The short duration of the attack and the factthat prior art pulmonary function tests can be deliberately manipulatedby the malingerer mean that there is low risk of fraud detection. Inshort, the intermittent, paroxysmal, and emergent features ofobstructive respiratory illness are especially congenial for fraud.

Once medical intervention in the ambulance or emergency departmentcommences, the fraudster undertakes to abate his/her simulated breathingabnormalities. The emergency care providers are satisfied and relievedthat their treatment has yielded the desired effect, they authordocumentation of that effect and deposit it in the patient's healthrecord, and the deception is complete. Among emergency medicinepersonnel in the U.S., the act of repeatedly mimicking physical signs ofmoderate to severe acute illness over a period of months to years withthe aim of fraudulently obtaining public disability payments is termed“pulling a Jenny-Sue.” Such deception costs society billions each year.Emergency medicine staff are understandably reluctant to assertmalingering when the available pulmonary function or other diagnostictests can be readily faked by the perpetrator and afford littleconfidence in discerning genuine illness from fraud. Therefore,improved, inexpensive diagnostics that are less susceptible tofraudsters and that are able to provide such discernment, such as can beprovided by some embodiments of the inventions are valuable and needed.

Prior attempts at pulmonary function testing methods that are brief andentail measurements over only one or a few breaths are highlysusceptible to fraud. For example, it is relatively easy for theperpetrator to know and mimic forced-expiratory maneuvers that willyield FEV1 and/or FVC values associated with moderate to severeobstructive lung disease. In that regard, some embodiments of thepresent invention are a substantial improvement upon the prior art.Moreover thirty or more minutes' capnometry nonlinearity measurements,such as may be obtained for certain embodiments, are less likely to bedeceived by malingerers. Specifically, it not plausible that a personcould conceive and carry out a pattern of breathing that would generatean abnormal capnometry nonlinearity properties resembling those ofgenuine obstructive lung disease, nor is it plausible that a healthyperson intent upon fraud could enact an abnormal pattern and maintain acomplex series of respiratory maneuvers over the course of 400 or morebreaths to successfully mimic obstructive respiratory impairment.Conversely, it is conceivable that an unhealthy person with actualobstructive lung disease might, with considerable and sustained effortaimed at mimicking a normal respiratory state, exhibit normal ornear-normal capnometry nonlinearity score. However, even thispossibility is decidedly unlikely during measurements lasting 30 minutesor more.

Some pulmonary medicine efforts have occasionally employed certainnonlinear analytics such as DFA; however, DFA in pulmonology hasprimarily addressed PEF, FEV1, and other non-capnometric measurementsand has not been directed to determining the risk of acute respiratorydistress or acute pulmonary function decompensation events requiringemergent attention by a physician.

Ventilation is influenced by gravity and body habitus, and thereforetraditional capnometry is notably susceptible to artifacts having to dowith transient changes in ventilation-perfusion matching. When thepatient is upright (standing or seated), abdominal contents are pulleddownward and the lower lobes of the lungs receive most of the blood flowwhile the upper lobes receive most of the ventilation. With supine orsemi-reclining positions, the diaphragm excursion into the belly is lessand ventilation and perfusion disparities between upper and lower lobesare reduced, resulting in smaller difference between venous pCO2 andetCO2.

Other sources of confounding of approaches to the problem includepulmonary compliance, psychological factors, and patient cooperation.Airway constriction in reaction to stressful or anxiety-provokingstimuli in asthma depends on an intact cholinergic pathway, is chieflyassociated with the central airways, and largely eludes detection byconventional measures of airway physiology, including traditionalspirometry, oximetry, and capnometry. Some embodiments of the presentinvention are able to detect emotionally-induced obstructive effects,largely owing to the nonlinear time series method and a substantiallylonger sampling interval compared to conventional exams.

Furthermore, it is widely known that persons being examined in a medicalsetting with capnometry during spontaneous breathing may breathe in sucha way as to exhibit larger-than-normal tidal volumes, due to thepatients' wish to conform to what they perceive to be the desire of theclinician who is examining them. While these patterns of alteredbreathing may be sustained by the patient for exams that only last a fewtens of seconds, it is highly improbable that such patterns will besustained for tens of minutes. Thus, some embodiments of the presentinvention tend to reflect the natural diversity of breathing patternsthat prevail over observation periods that are 30 or more minutes inlength. Such longer observation intervals afford the possibility tomeasure patterns that arise with lower frequency than are within thescope of traditional examination methods that only acquire data overshort periods of a few seconds.

Conventional capnometry-based monitoring systems have been shown to haveinadequate statistical sensitivity and specificity for the purpose ofpredicting respiratory events. When measurements rely upon spirometry oroximetry or conventional capnometry patterns as the trigger or sentinelevent for predicting incipient pulmonary function decompensation, thepredictions are generally only relevant when the person is alreadyexperiencing significant pulmonary function deterioration.

Additionally, many prior approaches involve cumbersome, complex,expensive and/or invasive instrumentation, or require a skilled operatorin attendance. For example, the most accurate approachs, such asisotopically-labeled gas diffusion methods, are expensive, are notwidely available, are only performable by subspecialty-trainedproviders, and are only applicable to a small subset of patients who arealready known to be at high risk.

Other recently-introduced efforts involve expensive measurements, suchas genomic or proteomic laboratory tests that are not widely availableand that have a performance turnaround time of many hours or days beforethe results and prediction are available for use, such that theprediction or classification is not timely with respect to interventionsaimed at preventing the predicted occurrences.

Additionally, some approaches are sensitive to, and may be compromisedor entirely confounded by, individual variations in patient anatomy andactivities, such as transfers from chairs or wheelchairs or beds,transfers with slide-boards or grab-bars other prosthetics, patientmovement and positioning, diurnal variations, etc. Similarly, themethods of these approaches may be compromised or entirely confoundedby, individual variations in operator positioning of the capnometercircuit on the patient or variations in the timing and method ofacquiring the specimens or data that will enter into the prediction andclassification.

Furthermore, one major deficiency of other approaches is false-negativeerror rate and the absence of immunity to differences in dailyactivities and behavior mix. Yet another deficiency isactivity-specificity, for example, the ability to detect or predictpulmonary function decompensation while walking but not while climbingstairs or running.

Still a further deficiency arising from the fact that existing systemsonly take measurements during a very brief exam is that they do not takeinto account diurnal variations in persons' capabilities. By contrast,some face-mask circuit and mobile computing device-enabled embodimentsof the present invention are wearable over periods of an hour or severalhours and, thereby, achieve sensitivity to time-varying patterns inrespiratory-risk.

Still further, no mathematical or biomechanical models have to-dateappeared that are able to predict pulmonary function decompensation froma wheelchair or other prosthetic devices that are prevalent inrehabilitation or long-term care venues.

Moreover, none of the other approaches has examined mathematicalnonlinearity properties of the measured capnometry time series, nor hasthe prior art made use of continuous realtime time series over shortperiods of many minutes for the purpose of determining likelihood ofnearterm future exacerbations of pulmonary function abnormalities.Despite the existence of capnometer recording equipment for more than 20years, the analysis of long-timeseries capnometry data is seldomperformed and, even then, it is used only for management of hospitalin-patients in intensive care units, titration of mechanical ventilatorsettings, and the like. Only small selected portions of the recordedtimeseries data may be subjected to detailed analysis, and the rest aregenerally discarded unexamined or ignored. Accordingly, a system(including an apparatus) or method that is inexpensive, non-invasive,accurate in its characterization of pulmonary function decompensationrisk based on timeseries acquired over several tens of minutes' time,and that accommodates a wide range of body morphologies and variationsin breathing mechanics is needed and would be welcomed. In particular,such systems or methods, as provided by embodiments of the invention,would facilitate preventing important adverse outcomes, and betterconserving care provider resources than typically would otherwise happenwith respiratory events risk estimation by the methods of otherapproaches.

In light of the foregoing, improved predictive-preventive methods andsystems are provided, and in some embodiments comprise predictionclassification or decision-support alert signals emitted at logisticallyconvenient times far enough in advance (such as hours or days, or weeks,in some embodiments) of a respiratory event's occurrence to allow forclassification of risk and effective preventive intervention in amajority of cases. Moreover, these embodiments provide additionaladvantages over other approaches, including that they are lessexpensive; amenable to use in an outpatient physician office or clinicwith limited space and staff resources, and suitable for a much largerpopulation who are at only a moderate risk of respiratory events.Accordingly, such embodiments find use as a tool not only forsurveillance and triaging the patients who present with respiratorycomplaints to hospitals and other acute-care venues but also forambulatory, free-living individuals who have one or more risk-factorsfor respiratory events.

Regarding respiratory preventive interventions, effective interventionsvary, and optimal selection and personalized tailoring of them candepend upon the patient's context, gender, age, medications, comorbiddiagnoses, history of previous respiratory events, and other factors. Inthe case of a moderately symptomatic ambulatory person, effectivepreventive interventions may include consultation with the personalphysician or nurse for adjustment of medications regimen or behavioraland activity recommendations, or presentation at a nearby outpatientdepartment for diagnostic assessment and monitoring. In the case of aperson with existing, known respiratory conditions, effective preventiveinterventions may include referral to a pulmonologist, consideration foradjustment of medication regimen, or other alternatives.

Certain embodiments are not so much intended for usage during an episodeof acute respiratory failure or during the first episode of anobstructive lung condition that is not yet chronic. Additionally,despite appropriate and compliant-adherent use of treatments such asβ2-adrenergic agonists, anticholinergics, and inhaled corticosteroidsand cognitive-behavioral measures, break-through episodes of acuterespiratory distress do still occur. Accordingly, some embodiments aimto predict and prevent a large percentage of these. Indeed, suchepisodes constitute one major focus of certain embodiments providedherein inasmuch as these embodiments are not primarily directed towardidentifying and diagnosing persons with previously unrecognized anduntreated obstructive lung disease, nor are they directed to themanagement issues of previously-diagnosed persons who have beenprescribed a suitable, effective regimen but who do not adhere to it.Rather, these embodiments may provide utility primarily (a) fortherapeutic decisions and medications titration in reasonablytherapy-adherent persons with refractory obstructive lung conditions ofsome months' or years' standing, in whom a propensity for recurringepisodes of decompensation is already manifest and (b) for evaluatingpossible non-adherence to a plausibly effective regimen.

In the large percentage of patients in whom upper respiratory tractinfections (URTIs) are the primary trigger for acute pulmonary functiondecompensation, embodiments of the invention may help to guidepreventive interventions for infection prophylaxis.

Some embodiments may also be of use in gate-keeping decisions regarding“step” therapy with agents such as immunotherapies (e.g., omalizumab) orphosphodiesterase-4 (PDE4) inhibitors (roflumilast) or bronchialthermoplasty, or in implementing intensified monitoring with pulmonaryfunction testing or frequent exhaled nitric oxide monitoring orcase-management services-interventions that are applied according tocost-effectiveness policies that select patients according to criteriathat warrant the incremental expense.

Turning now to FIGS. 1, 2A and 2B, exemplary operating environments foran embodiment the invention are described and relate generally to thedescription of a mobile wearable system suitable to be utilized fortimeseries nonlinearity properties-based prediction and prevention ofacute respiratory distress. Referring to the drawings in general, andinitially to FIG. 1 in particular, an exemplary operating environment100 is provided suitable for practicing an embodiment of our invention.We show certain items in block-diagram form more for being able toreference something consistent with the nature of a patent than to implythat a certain component is or is not part of a certain device.Similarly, although some items are depicted in the singular form, pluralitems are contemplated as well (e.g., what is shown as one data storemight really be multiple data-stores distributed across multiplelocations). But showing every variation of each item might obscure theinvention. Thus for readability, we show and reference items in thesingular (while fully contemplating, where applicable, the plural).

As shown in FIG. 1, environment 100 includes one or more sensors 116. Inone embodiment, sensor(s) 116 include one or more transducers or typesof sensors operable for providing electrical signals corresponding tomeasurements of various conditions or states of a user, as furtherdescribed below. Embodiments of sensor(s) 116 may further include apower supply, processor, memory operable for acquiring and storinguser-information and programming instructions, and communicationcomponent for communicating the resulting measurements ofuser-information with processing/communication component 130.

In some embodiments, sensor(s) 116 includes one or more capnometerictransducers operable to determine capnometer measurement of CO2 partialpressure and to provide signals corresponding to the time course ofchanging CO2 concentration in the stream of expired air. For example, insome embodiments, sensor(s) 116 includes one or more transducers, whichcan take the form of infrared phototransistor capnometer sensors, forobtaining electrical CO2 signals from the individual. In one embodiment,a non-dispersive infrared (NDIR) illumination source is utilized,consisting of either a broad-spectrum infrared source such as anincandescent lamp filament or glow-bar. NDIR embodiments employ anarrow-bandpass interference filter in the optical path, to restrict theincident light on the sensor to one of the bands in which CO2 exhibitssignificant absorption (e.g., 4.260 μm or 2.004 μm). In anotherembodiment, dispersive illumination from a narrow-spectrum infraredsource such as an LED diode or a laser diode is used, with said sourcehaving peak emission at the desired band. The spectrometry may be eithersingle-wavelength or dual-wavelength, and concentration of CO2 isdetermined from the optical absorbance measurements according to thewidely-known Beer's Law.

In some embodiments, a processor for sensor(s) 116 (which may beembodied as component 130, described below, or as a separate processorfor controlling one or more sensor(s) 116) is operable to control thefrequency of measurements; for example, to read a transducer's output atcertain intervals such as 10 or more times each second; to pre-processor condition the signal, including applying a threshold, noise-filter,or normalizing the raw user-derived signal; read from or store theuser-information in memory, and communicate the acquired timeseries ofuser-information to component 130 via a communication component ofsensor 116. In one embodiment, a floor-threshold is applied such thatonly measurements of a certain magnitude or within desired parametersare acquired and communicated to component 130. For example, it may bedesirable in some embodiments not to capture every breath of the user,but rather only breaths that exhibit morphology denoting the patient'sconformance to the sampling protocol.

In one embodiment, sensor(s) 116 include one or more capnometers, flowmeters, or combination of such devices as to enable one or moresensor(s) 116 to detect user breathing, user position or orientation,and sudden changes in user position. In these embodiments the timeseriesof user-information communicated to component 130 may compriseindividual capnometry CO2 concentration values, with each new CO2measurement adding a member to the timeseries. In other words, therecould be irregular periods of time between capnometrys that are capturedby sensor(s) 116.

In one embodiment, such a sensor 116 may be optimally positioned on theuser. In such an embodiment, a computing device, which may be embodiedas a mobile device, such as a smart phone, running a program fordetermining timeseries nonlinearity measures, may monitor usercapnometry timeseries and provide to the user and health-care providerearly earning warning of a likelihood of increased risk of acuterespiratory distress. In one embodiment, sensor(s) 116 are embodied asone or more capnometric devices, such as the Massimo Emma®, produced bythe Massimo Corporation of Irvine Calif., the CapONE TG-900 series,produced by Nihon-Kohden, Phillips Respironics NM3, or similar devices.Some of these devices also include functionality described in connectionto processing/communication component 130, described below.

Continuing with FIG. 1A, environment 100 includesprocessing/communication component 130, which is communicatively coupledto sensor(s) 116, storage 192 and backend 190. Exemplary embodiments ofcomponent 130 include one or more processors operable for processinguser-sensor information and determining capnometry measures andnonlinearity, a communication module for receiving information from oneor more sensor(s) 116 and for communicating results to the user orhealth-care provider, and a memory (which may be embodied as storage192) for storing received user-information, determined results, andprogramming instructions. In some embodiments, component 130 may worn onthe user's body, such as clipped to a belt, in a holster, or around theuser's waist, torso, or neck, or can be carried by the user, such as inthe user's pocket or purse, or may be kept with a close enough proximityto the user as to communicate with sensor(s) 116. In some embodiments,sensor(s) 116 are housed within or on component 130, and in someembodiments, one or more features or subcomponents of sensor(s) 116 andcomponent 130 may be integrated, thereby eliminating or reducing noise,interference, distortion, ambient atmospheric pressure, water vaporpressure, or artifacts and also improving ease-of-use and patientcompliance.

Some embodiments of component 130 comprise a smart phone running one ormore application programs or “apps” for receiving user-sensorinformation, determining capnometric-related measures or statistics, andstoring and communicating results to the user and health care provider.In some smart-phone embodiments, component 130 uses the phone'scommunication equipment for communicating user information to a backend190, such as a health care provider or decision-support knowledge agent.Component 130 may use other communication features of the smart phonesuch as Bluetooth or Wi-Fi to communicate with one or more sensors 116and in some embodiments, a base station, user computer, oruser/clinician interface, such as described in connection to FIG. 2A.

A smart phone may be communicatively-coupled with an additionalcomponent for facilitating communication with one or more sensors 116,for processing user-information, or for storing and communicating userresults. For example, in one embodiment, component 130 iscommunicatively-coupled to a holster or other component containing acommunication module for communicating with one or more sensor(s) 116.Such an embodiment is useful where sensor(s) 116 use a communicationprotocol that is not compatible with component 130. For example, wheresensors communicate using Bluetooth, but component 130 is embodied onnon-Bluetooth enabled smart phone, the user may attach a Bluetoothmodule to the smart phone to enable it to communicate with sensors 116.Similarly, where sensor(s) 116 communicate using Zigbee or anotherlow-rate wireless personal area network platform, a user may couple aZigbee-enabled communication module to their smart phone. In anotherexample embodiment, a smart phone may be communicatively-coupled with abase station (not shown) located in the user's house. In one embodiment,the base station could be a personal computer connected to a wirelessrouter or a laptop equipped with RF communication capability such asWi-Fi or Bluetooth. In one embodiment, the base station communicateswith backend 190.

In another embodiment, processing/communication component 130communicates directly with backend 190. Backend 190 includes the healthcare provider computer system and devices, case-management software,electronic health record decision-support systems and devices, andconsumer personal health record systems and devices. In someembodiments, brick 130 stores information on data store 192, which maybe local or remotely located, and which may be accessible by backend190, in some embodiments. In some embodiments, data store 192 comprisesnetworked storage or distributed storage including storage on serverslocated in the cloud. Thus, it is contemplated that for someembodiments, the information stored in data store 192 is not stored inthe same physical location. For example, in one embodiment, one part ofdata store 192 includes one or more USB thumb drives or similar portabledata storage media. Additionally, information stored in data store 192can be searched, queried, analyzed via backend 190, such as by a healthcare provider or by a decision-support knowledge agent, for example.

In some embodiments, sensor(s) 116 communicate with other sensors 116and with component 130 over a wired or wireless communication protocol.In one embodiment, sensor(s) 116 communicate using Bluetooth, Wi-Fi, orZigbee protocols. In some embodiments a low-powered communicationprotocol is desirable in order to preserve the batter life of thesensor(s) 116. In some embodiments using a communication protocol havinga narrow bandwidth, such as Zigbee, sensor(s) 116 may also include amemory buffer for storing user-derived information until it iscommunicated to component 130. Sensors 116 may also communicate withother sensors 116 or directly with a base station, in some embodiments.

As described above, embodiments of processing/communication component130 are communicatively coupled to one or more sensor(s) 116, such as awearable capnometry sensor, which is one embodiment of sensor(s) 116. Insome embodiments, component 130 and/or sensor(s) 116 are capable ofbeing coupled to a docking station or base station (not shown), whichrecharges a battery or other power supply in component 130 and orsensor(s) 116.

Turning now to FIG. 2A, an aspect of an operating environment suitablefor practicing an embodiment of the invention is shown and referencedgenerally as 101. Example operating environment 101 includes acomputerized system for compiling and/or running an embodiment of anacute respiratory decompensation event prediction service, and may beused for generating an ensemble classifier and verifying and validatingwhether such a detector achieves statistical sensitivity and specificityin the intended mortality range of deployment, sufficient forsatisfactory performance in the use for classifying patients accordingto in-hospital mortality outcome.

With reference to FIG. 1C, one or more electronic health record (EHR)systems, such as hospital EHR system 160 are communicatively coupled tonetwork 175, which is communicatively coupled to computer system 120. Insome embodiments, components of operating environment 101 that are shownas distinct components may be embodied as part of or within othercomponents of environment 101. For example, EHR system(s) 160 maycomprise one or a plurality of EHR systems such as hospital EHR systems,health information exchange EHR systems, ambulatory clinic EHR systems,psychiatry/neurology EHR systems, and may be implemented in computersystem 120. Similarly, EHR system(s) 160 may perform functions for twoor more of the EHR systems (not shown) in FIG. 2A.

In embodiments, network 175 includes the Internet, and/or one or morepublic networks, private networks, other communications networks such asa cellular network, or similar network(s) for facilitating communicationamong devices connected through the network. Network 175 may bedetermined based on factors such as the source and destination of theinformation communicated over network 175, the path between the sourceand destination, or the nature of the information. For example,intra-organization or internal communication may use a private networkor virtual private network (VPN). Moreover, in some embodiments itemsshown communicatively coupled to network 175 may be directlycommunicatively coupled to other items shown communicatively coupled tonetwork 175.

In some embodiments, operating environment 101 may include a firewall(not shown) between a first component and network 175. In suchembodiments, the firewall may reside on a second component locatedbetween the first component and network 175, such as on a server (notshown), or reside on another component within network 175, or may resideon or as part of the first component.

Embodiments of electronic health record (EHR) system(s) 160 include oneor more data stores of health records, which may be stored on storage121, and may further include one or more computers or servers thatfacilitate the storing and retrieval of the health records. In someembodiments, EHR system(s) 160 may be implemented as a cloud-basedplatform or may be distributed across multiple physical locations. EHRsystem(s) 160 may further include record systems, which store real-timeor near real-time patient (or user) information, such as wearable,bedside, or in-home patient monitors 141, for example.

Although FIG. 1A depicts an EHR system(s) 160, it is contemplated thatsome embodiments may rely on monitor interface 140 and/or patientmonitor 141 for storing and retrieving patient record information suchas information acquired from patient monitor 141.

Example operating environment 101 further includes provideruser/clinician interface component 142 communicatively coupled tonetwork 175. Embodiments of interface 142 may take the form of auser-clinician interface operated by a software application or set ofapplications on a client computing device such as a personal computer,laptop, smartphone, or tablet computing device. In one embodiment, theapplication includes the PowerChart® software, manufactured by CernerCorporation. In an embodiment, the application is a Web-basedapplication or applet. A provider clinician application facilitatesaccessing and receiving information from a user or health care providerabout a specific patient or set of patients for which arespiratory-event-risk assessment is to be performed and facilitates thedisplay of results, recommendations or orders, for example. In someembodiments interface component 142 also facilitates receiving ordersfor the patient from the clinician/user, based on the results. In someembodiments, interface component 142 may also be used for providingdiagnostic services.

Example operating environment 101 further includes computer system 120,which may take the form of a server, which is communicatively coupledthrough network 175 to EHR system(s) 160, storage 121, and monitorinterface component 140.

Embodiments of user monitor interface component 140 may take the form ofa user interface and application, which may be embodied as a softwareapplication operating on one or more mobile computing devices, tablets,smart-phones, front-end terminals in communication with back-endcomputing systems, laptops or other computing devices. In someembodiments, monitor interface component 140 includes a Web-basedapplication or set of applications that is usable to manage userservices provided by embodiments of the invention. For example, in someembodiments, monitor interface 140 facilitates processing, interpreting,accessing, storing, retrieving, and communicating information acquiredfrom patient monitor component 141. In some embodiments, monitorinterface 140 is used to display user (or patient) information relatingto breathing and risk for acute respiratory decompensation event. Insome embodiments of monitor interface 140, an interface component may beused to facilitate access by a user to functions of information onpatient monitor 141, such as operational settings or parameters, useridentification, user data stored on monitor 141, and diagnostic servicesor firmware updates for monitor component 141, for example.

As shown in example environment 101, monitor interface component 140 iscommunicatively coupled to patient monitor component 141 and to network175. Embodiments of patient (or user) monitor component 141 may compriseone or more sensor(s) 116, described in connection to FIG. 1, and insome embodiments, monitor component 141 and interface component 140comprise processing/communications component 130 and one or moresensor(s) 116, described in connection to FIG. 1.

Embodiments of monitor component 141 may store user-derived data locallyor communicate data over network 175 to be stored remotely. In someembodiments, monitor interface component 140 is wirelesslycommunicatively coupled to monitor component 141. Monitor interface 140may also be embodied as a software application or app operating on auser's mobile device, and in an embodiment may facilitate uploading ofmotion information from monitor 141 to computer system 120. In someembodiments, monitor interface 140 and monitor 141 are functionalcomponents of the same device, such as a device comprising a sensor anda user interface. In some embodiments, monitor interface 140 is embodiedas a docking station or base station, which may also includefunctionality for charging monitor 141 or downloading information frommonitor component 141. In some embodiments, monitor interface 141 is notpart of the application on computer system 120 for interfacing withmonitor 141 (sensor(s) 116), such as receiving motion information frommonitor 141.

As previously described, in some embodiments patient monitor component141 and monitor interface component 140 are embodied an sensor(s) 116and processing/communication component 130. One embodiment comprisescapnometric devices, such as Massimo Emma®, produced by the MassimoCorporation of Irvine Calif., the CapONE TG-900 series, produced byNihon-Kohden, Phillips Respironics NM3, or similar devices, whereininterface component 140 comprises the user-interface functionalityprovided by these devices.

Continuing with FIG. 2, some embodiments of monitor component 141 and/ormonitor interface component 140 include functionality for processinguser-derived information locally or for communicating the information tocomputer system 120, where it is processed. In some embodiments, theprocessing may be carried out or facilitated by one or more softwareagents, as described below. In some embodiments the processingfunctionality, performed by processing/communication component 130,which may occur on monitor component 141, monitor interface 140, and/orcomputer system 120 includes signal conditioning, such as removing noiseor erroneous information.

Computer system 120 comprises one or more processors operable to receiveinstructions and process them accordingly, and may be embodied as asingle computing device or multiple computing devices communicativelycoupled to each other. In one embodiment, processing actions performedby system 120 are distributed among multiple locations such as one ormore local clients and one or more remote servers. In one embodiment,system 120 comprises one or more computing devices, such as a server,desktop computer, laptop, or tablet, cloud-computing device ordistributed computing architecture, a portable computing device such asa laptop, tablet, ultra-mobile P.C., or a mobile phone. In oneembodiment, computer system 120 comprises processing/communicationcomponent 130 and/or backend 190, described in FIG. 1, or aspectsthereof.

Embodiments of computer system 120 include computer software stack 125,which in some embodiments operates in the cloud, as a distributed systemon a virtualization layer within computer system 120, and includesoperating system 129. Operating system 129 may be implemented as aplatform in the cloud, and which is capable of hosting a number ofservices such as 122, 124, 126, and 128. Some embodiments of operatingsystem 129 comprise a distributed adaptive agent operating system.Embodiments of services 122, 124, 126, and 128 run as a local ordistributed stack in the cloud, on one or more personal computers orservers such as system 120, and/or a computing device running interfaces140 and 142. In some embodiments, interface 140 and/or interface 142operates in conjunction with software stack 125.

In embodiments, variables indexing (or mapping) service 122 provideservices that facilitate retrieving frequent item sets, extractingdatabase records, and cleaning the values of variables in records. Forexample, service 122 may perform functions for synonymic discovery,indexing or mapping variables in records, or mapping disparate healthsystems' ontologies, such as determining that a particular medicationfrequency of a first record system is the same as another record system.In some embodiments, these services may invoke software services 126. Inone embodiment software stack 125 includes predictive models service124, which comprises the services or routines for determiningnonlinearity measures of capnometry-information orrespiratory-related-information timeseries and performing an ensemble ofmeasures, such as example program 700 illustratively provided in FIGS.7A-7C.

Software services 126 perform statistical software operations, andinclude statistical calculation packages such as, in one embodiment, theR system (the R-project for Statistical Computing, which supportsR-packages or modules tailored for specific statistical operations, andwhich is accessible through the Comprehensive R Archive Network (CRAN)at http://cran.r-project.org); R-system modules or packages includingfor example, nonlinear Tseries, nlts (nonlinear time series), andwaveslim packages, or similar services. In some embodiments, softwareservices 126 are associated with file system frameworks, such asnon-distributive file systems or distributive file systems, such asprovided the Apache Hadoop and Hbase framework, which in someembodiments facilitate provide access to cloud-based services such asthose provided by Cerner Healthe Intent®.

Example operating environment 101 also includes storage 121 or datastore 121, which in some embodiments includes patient data for acandidate or target patient (or information for multiple patients);variables associated with patient recommendations; recommendationknowledge base; recommendation rules; recommendations; recommendationupdate statistics; an operational data store, which stores events,frequent itemsets (such as “X often happens with Y”, for example), anditem sets index information; association rulebases; agent libraries,solvers and solver libraries, and other similar information includingdata and computer-usable instructions; patient-derived data; and healthcare provider information, for example. It is contemplated that the termdata includes any information that can be stored in a computer-storagedevice or system, such as user-derived data, computer usableinstructions, software applications, or other information. In someembodiments, data store 121 comprises the data store(s) associated withEHR system 160 and/or storage 192 of FIG. 1. Further, although depictedas a single storage data store, data store 121 may comprise one or moredata stores, or may be in the cloud.

Turning briefly to FIG. 2B, there is shown one example embodiment ofcomputing system 900 that has software instructions for storage of dataand programs in computer-readable media. Computing system 900 isrepresentative of a system architecture that is suitable for computersystems such as computing system 120. One or more CPUs such as 901, haveinternal memory for storage and couple to the north bridge device 902,allowing CPU 901 to store instructions and data elements in systemmemory 915, or memory associated with graphics card 910, which iscoupled to display 911. Bios flash ROM 940 couples to north bridgedevice 902. South bridge device 903 connects to north Bridge device 902allowing CPU 901 to store instructions and data elements in disk storage931 such as a fixed disk or USB disk, or to make use of network 933 forremote storage. User I/O device 932 such as a communication device, amouse, a touch screen, a joystick, a touch stick, a trackball, orkeyboard, couples to CPU 901 through south bridge 903 as well. Thesystem architecture depicted in FIG. 2B is provided as one example ofany number of suitable computer architectures, such as computingarchitectures that support local, distributed, or cloud-based softwareplatforms, and are suitable for supporting computing system 120.

Returning to FIG. 2A, in some embodiments, computer system 120 is acomputing system made up of one or more computing devices. In someembodiments, computer system 120 includes one or more software agents,and in an embodiment includes an adaptive multi-agent operating system,but it will be appreciated that computer system 120 may also take theform of an adaptive single agent system or a non-agent system. Computersystem 120 may be a distributed computing system, a data processingsystem, a centralized computing system, a single computer such as adesktop or laptop computer or a networked computing system.

Turning now to FIG. 3A, a flow diagram is provided illustrating oneexemplary method 300, in accordance with an embodiment of the invention.As described previously embodiments of the invention provide acomputerized system, methods, and computer-readable media forautomatically identifying persons who are at risk for pulmonary functiondecompensation through the use of a system, which in one embodiment,includes noninvasive, portable, wearable electronic device and sensorsequipped with signal-processing software and statistical predictivealgorithms that calculate timeseries nonlinearity measures, derived froma digital capnometric-signal timeseries acquired by the device. Themeasurements and predictive algorithms embedded within the deviceprovide for unsupervised use in general acute-care and chronic-carevenues and afford a degree of robustness against variations inindividual anatomy and sensor placement.

Some embodiments are therefore able to provide a leading indicator ofnear-term future abnormalities, proactively alerting the clinicianscaring for the person with sufficient advance notice to enable effectivepreventive maneuvers to be undertaken. In one exemplary embodiment, thedevice is integrated with case-management software and electronic healthrecord decision-support system.

By way of example and not limitation, a user using one embodiment of theinvention may be able to breathe into the capnometry sensor apparatus inan outpatient office or clinic for a short interval of time, such as 30minutes for example, during which capnometry measurements are acquiredfor 400 or more breaths taken by the person, digitized at a samplingrate preferably at 10 Hz or greater at 12 bits precision or more. In oneembodiment, the user may don one or more sensors capable of acquiringcapnometer measurements, which could include a sensor affixed via amouthpiece and check-valved breathing circuit or by similarly-equippednasal prongs or a facemask that seals tightly about the subject's mouthand nose, and the acquired sensor data are temporarily stored in amemory unit in the sensor device itself (e.g. sensor(s) 116) and latercommunicated from the device to a processing system (e.g., computersystem 120 and/or processing/communication component 130).

In one example embodiment, the computer system may include one or moresoftware program services, which may be embodied as an application orapp, which when executed receives user data from the sensor-device orstorage 121, calculates a plurality timeseries nonlinearity measures,combines these in a mathematical ensemble model, and communicates thecomposite ensemble results to the clinician user, case-managementsoftware, decision-support systems, or electronic health record systems.For example, the system may notify the user in advance, via anotification message or electronic mail, and may also notify the user'shealth plan, electronic-health record decision-support systems orpersonal health record systems, via a call, HTTP, SMS text-message, orother form of electronic or radiofrequency communication, that the userhas an increased likelihood of a near-term future abnormality orrespiratory occurrence. This enables the care providers to takeappropriate preventative measures.

With reference to FIG. 3A, at a high level, method 300 illustrativelydepicts a method for determining a capnometry nonlinearity score (CNS)for an individual. The CNS may be determined by applying an objectivefunction to user-derived information such as capnometry-signalinformation obtained from the one or more sensors 116. Some embodimentsof the invention process the information to calculate a CNS(t)timeseries, where t represents time, as a function of the individual'sinstantaneous CNS determinations.

Method 300 uses an ensemble model based on a poll or “vote”, such as butnot limited to a “majority vote”, although this is shown, among aplurality of measures of capnometry time series nonlinearity as acontinuous or discrete function of time is utilized, such as theLikelihood Ratio metric, White test, Teräsvirta metric, McLeod-Limetric, Tukey 1-df metric, and time-reversibility metric are exemplaryquantitative measures of the nonlinearity of timeseries. Relevant testsof the nonlinearity of timeseries as are known to those practiced in theart. In some embodiments, these may include: Likelihood Ratio test forthreshold nonlinearity; White neural network test for nonlinearity;Teräsvirta neural network test for nonlinearity; McLeod-Li test forautoregressive conditional heteroscedascity (ARCH); Third-ordertime-reversibility statistic; Tsay's test for quadratic nonlinearity;Hinich test; Subba Rao-Gabr test; and/or Keenan's one-degree test fornonlinearity.

Prior to the method 300, a patient monitor 141, is initialized andattached to a user-patient, in some embodiments. For example, the sensormay be configured for the patient including adjusting sensitivity basedon the particular patient or location(s) or types of the sensor(s) 116of monitor 141. In some embodiments, monitor 141 may be configured so asto provide information to an EHR 160 associated with the particularpatient.

Following initialization of monitor 141, at a step 310, capnometrysignals of a user, such as a patient, are obtained using one or moresensors 116. In one embodiment, user-information representative of thecapnometry signals is communicated from one or more sensors 116 tocommunication/processing component 130 or storage 121, where it may besubsequently processed. In one embodiment, sensor 116 capturescapnometry waveforms corresponding to the user's movement, therebyresulting in a timeseries of capnometry-signal intervals. In someembodiments, information from approximately 400 breaths or about 30minutes of breathing is obtained to accumulate capnometric orrespiratory information.

At step 315, the received capnometry information may be cleaned orcensored, if necessary or desired. For example, it may be necessary ordesirable to scrub, eliminate outliers of received capnometryinformation or delete certain samples.

In steps 320 through 345, the capnometry nonlinearity score (CNS) as afunction of the continuous or discrete capnometric timeseries iscalculated. De-meaning is applied to remove baseline offset from theresulting signal, in some embodiments. Normalizing the maximal value ofdifferences to the absolute magnitude of the signal may also beperformed, in some embodiments, before calculating and updating theCNS(t) timeseries. Instructions carried on a computer-readable storagemedium (e.g., for calculating CNS(t)) can be implemented in a high levelprocedural or object oriented programming language to communicate with acomputer system, in one embodiment, such as example program 700 providedin FIGS. 7A-7C. Alternatively in another embodiment, such instructionscan be implemented in assembly or machine language. The language furthercan be compiled or interpreted language, in one embodiment.

It is further contemplated that in some embodiments, the CNS-relatedprocessing occurring in steps 320 through 345 occurs in realtime or nearrealtime, simultaneously, as electrical capnometric signal-informationis collected in step 310, thereby allowing a skilled operator to monitoran individual's CNS during pharmacologic or exercise physiologic stress,if desired. More generally, in some embodiments, processing steps 320through 345 are performed substantially simultaneously with the step 310of collecting the capnometric signals in near real-time, so as to enablethe ambulatory patient to continue their ad lib breathing until at least30 minutes have elapsed or until data from at least 400 breaths haveaccumulated.

At step 320, the received capnometric signals, comprisingcapnometry-information timeseries, are prepared for the nonlinearitymeasures. In some embodiments this preparation includes pre-processingor signal conditioning. Step 320 may be performed by sensor 116, byprocessing/communication component 130, or a combination. Inembodiments, thresholding, artifact censoring, normalizing, noisefiltering, or other DSP filtering, or any combination of these, may beapplied to the raw signal information.

In one embodiment, ascertainment of the boundaries denoting thebeginning and ending of a respiratory cycle is performed byfirst-derivative zero-crossing calculations or by the method of finitedifferences. When a new expiratory phase commences, the first gas to beexpelled from the respiratory tree into the capnometer is air that hasremained in the larger airways and throat, where little or no gasexchange occurs (“anatomical dead volume”). The CO2 concentration in theanatomical dead volume is very nearly equal to the CO2 concentration inambient room air (˜380 to 2,000 ppm). As such, the capnometry waveformat the onset of expiration is near ambient, and the onset isidentifiable via the zero-crossing or thresholding of the first timederivative of the CO2 signal.

At a step 330, the ensemble of j timeseries nonlinearity measures isdetermined. In step 330 of j nonlinearity measures is first determined,or attempted, and compared to a reference value where successfullycompleted. Then an ensemble, such as described above, is performed onthe k nonlinearity measures to determine a CNS score for the patient.

In particular, in some embodiments of step 330, the CNS timeseries isdetermined, a plurality of timeseries nonlinearity metrics arecalculated, and used to determine the patient's acute risk ofrespiratory distress. By way of example and not limitation, themethodology of the invention may be understood through the followingsteps. Let the metrics consist of (a) the Likelihood Ratio test [LRT],(b) the White test, (c) Teräsvirta's test, (d) the McLeod-Li test, (e)Tukey's 1-df test, and (f) a time-reversibility test, all applied to aseries of segments of the data comprising “moving windows” at least 100breaths in length. Calculate p-values of the tests and accept the H1alternate-hypothesis of “abnormal, deficient nonlinearity” if amajority-vote asserting abnormality is achieved among the following:LRT>1*10-3, White>1*10-2, Teräsvirta>1*10-2, McLeod-Li<1*10-4,Tukey>5*10-3, time-reversibility>−1*10-3. Each nonlinearity metric fortimeseries acquired from low-risk ‘normal’ walking subjects (ones who donot experience a capnometric pulmonary function decompensation event inthe near-term) has a value within a characteristic normal range forhuman ambulation, and this normal range of mild nonlinearity is for eachmetric reasonably insensitive to variations in age, gender, bodymorphology and size, time of day, over-the-ground walking surface,walking surface grade, and other factors. Linear or Gaussian-distributedtimeseries properties denote or confer an increased risk of pulmonaryfunction decompensation, as do severely nonlinear timeseries properties.By contrast, mild nonlinearity within the characteristic range for eachmetric is rarely associated with near-term respiratory events. In anembodiment the timeseries length M may vary between 400 to 1,000samples, with accuracy generally increasing as the size of M increases.

In some embodiments of step 330, when a plurality of these metricstransgress their respective normal ranges which denote mild nonlinearityof normal, stable CO₂ gas exchange, the near-term likelihood of acuterespiratory distress is increased, regardless whether the causation ofrespiratory events arises due to obstructive abnormalities in thebronchi and bronchioles of the respiratory tree, in the deep lungparenchyma, or via secondary causes, such as dementia, or neuromuscular,skeletal deformity, or other reasons.

In another embodiment, the evidence-combining method entails arithmeticaveraging or median or other means of deriving a composite measure fromthe plurality of individual metrics in such a manner as to accuratelyreflect the predominant tendency or risk level. In another embodiment,the evidence-combining method entails a weighted linear sum reflectingthe possibly non-uniform predictive evidentiary strength that isassociated with the individual metrics, such as may arise due to varyingsensitivity and/or specificity of the metrics, which may depend on thelength of the timeseries, the number of steps encompassed by thetimeseries, or factors pertaining to the power and efficiency of themetric's ascertainment of nonlinearity of the timeseries.

An example embodiment of computer instructions for carrying out step 330(and other steps of the method 300) is illustratively provided in FIGS.7A-7C as program 700. In this example, for each of the nonlinearitymeasures, rather than take the average or some other central tendency,the median is determined for each measure, and then compared to areference value characteristic for normal human capnometry based onother parameters associated with the user, the venue (in some instancescertain locations have elevated CO2 levels, such as may be produced bycertain types of heating), or other factors.

At a step 335, in some embodiments a determination is performed as towhether enough nonlinearity measures have been obtained so as to performthe ensemble operation. In some situations, where j nonlinearitymeasures are undertaken, only k of them may compute or otherwise besensitive of specific enough as to be used; some of them (j-k of them)may not converge, may be too insensitive/non-specific, or may otherwiseyields unuseable results. Thus for example, where 6 (j) nonlinearitymeasures are performed, only 3 (k) may yield a usable result.Accordingly, in this embodiment, at step 335 a determination is made asto whether enough nonlinear measures are available (j≥k>2; thus k=3 issufficient). Where this condition is not satisfied, another nonlinearitymeasure may be undertaken. In some embodiments, the method proceeds tostep 310, and capnometry information is received again. In someembodiments, the method proceeds instead back to step 330 (not shown)and one or more additional nonlinearity measures is performed on thealready received motion information.

At a step 345, the “voting” or polling of the nonlinearity measures isperformed to determine—in an embodiment—whether a majority of thenonlinearity measures indicate an elevated risk for a respiratorydecompensation event. In some embodiments, only more than half of thenonlinearity measures need indicate instability, while in otherembodiments, only one or another minimum number need indicateinstability, wherein the minimum number is based on the particularnonlinearity measure, the patient, and clinician/caregiver preferences.In some embodiments, each nonlinearity measure gets one vote, and thevotes are totaled, while in some embodiments, the measures are combinedbased on weights, which are determined based on past performance orclinician/caregiver settings. In an embodiment, a CNS or the determinedrisk for an acute respiratory decompensation event, determined at steps330-345, is determined based on a ratio of the results of thenonlinearity models that were successfully calculated. For example,where each nonlinearity model (or measure) gets one vote and the votesare totaled, then where greater than 50% of the models indicate anabnormal result, then the patient is determined to have an elevated riskof respiratory distress.

In some embodiments, of step 345 it is determined whether the user'scapnometry timeseries is showing signs of abnormal nonlinearity, basedon the results of step 330. In one embodiment, if the stability ispresent, then the process returns to step 310 and additional capnometrytimeseries information is obtained from monitor 141 (or from sensors116).

If at step 345, the results of step 330 indicate the presence ofabnormal pulmonary function decompensation-risk related nonlinearity,then the method proceeds to step 350.

At a step 350, a user, health care provider, or decision support systemis notified that the user has an elevated acute risk of respiratorydistress. In one embodiment, this instability indicates a change in thepatient's condition, which may be for the better or worse. In oneembodiment, a visual or graphical display of the electrical signals or anumerical or digitized representation of the monitored capnometryvariables and CNS indices may be presented on a user's computercommunicatively coupled to component 130, or a health care provider'scomputer communicatively coupled to backend 190. In one embodiment, aradiofrequency message may be emitted tosecurity-/confidentiality-controlled, mated transceivers such asBlueTooth smartphones, Wi-Fi connections with personal computers orelectronic medical records systems, and similar devices.

On the other hand, if at step 345, the results of step 330 do notindicate the presence of abnormal respiratory-distress-risk relatednonlinearity, then the method proceeds to step 360, and the determinedrisk is recoded as within normal range.

At a step 375, in some embodiments, it is determined whether the patientis to be retested. Where the patient is not retested, the methodproceeds to step 390; and where the patient is retested, the method mayproceed ultimately back to step 310, through step 370, where thetimeseries of capnometry is obtained again. In some embodiments, step370 may specify parameters for repeating the method 300, such as theduration of time (or number of breaths) for obtaining the capnometrysignal information.

Turning to FIG. 3B a flow diagram is provided illustrating an exemplarymethod for automatically predicting elevated risk of respiratorydistress, according to one embodiment, generally referred to herein asmethod 301. At a high level, method 301 illustratively depicts a methodfor determining a capnometry nonlinearity score (CNS) for an individual.The CNS is determined by applying an objective function to user-derivedinformation such as capnometric signal information obtained from one ormore sensors 116. The method also includes determining the differencebetween the stability score value and a reference value to detectpresence of instability of capnometry dispersion or other measurements.It has been determined, as further described below in connection to thata significant difference between the two values indicates an increasedrisk of pulmonary function decompensation for an individual. In oneembodiment, the reference value is selected based on other parametersassociated with the user.

At a step 311, capnometry signals of a user are obtained using one ormore sensors 116. User-information representative of the capnometrysignals is communicated from one or more sensors 116 toprocessing/communication component 130. In some embodiments,pre-processing and conditioning of the capnometry signal information,which may include, for example, thresholding or flooring, artifactcensoring, normalization, or DSP filtering, and other pre-processing andconditioning as described in connection with steps 315 and 320 in FIG.3A, takes place either at the sensor 116, component 130, or both. At astep 321, CNS is determined in accordance with the method described inconnection to steps 320 to 345 of FIG. 3A. At a step 351, the differencebetween the CNS and a reference value is determined. In one embodiment,the reference value is selected based one or more parameters associatedwith the user (or patient), the respiratory condition, testingenvironment, or preferences of the caregiver. In one embodiment, thereference value is set by the health care provider. Based on the resultsof this difference, at a step 361, a determination is made as to whetherthe difference is significant. In one embodiment, the difference issignificant if the CNS exceeds the reference value. If the difference isnot significant, then method 301 may terminate or may return to step 311and wait to obtain additional signal information. On the other hand, ifthe difference is significant, then at a step 371, a notification may beprovided such as described in connection to step 350 of method 300, inFIG. 3A.

Turning now to FIG. 4, a portion of an example waveform representing amixed-expiratory mainstream-sampled feCO₂ timeseries is provided andreferred to generally as capnography curve 450. Curve 450 includes aseries of respiratory cycles 460, each comprising an expiration portion462 and inhalation portion 464. Each cycle 460 includes an end-tidalplateau phase 440. Curve 450 illustratively represents digitizedmeasurements of an analog fractional expired carbon dioxide (feCO₂)signal, such a may be provided by sensor(s) 116, which in this instanceare digitized at 100 Hz sampling rate using a commercially-available12-bit analog-to-digital conversion module. In some embodiments, 400end-tidal plateau phase 440 values are used for the capnometrytimeseries. In one embodiment the last three values before a firstderivative zero crossing are excluded.

By way of example, one embodiment of the invention, such as described inconnection to FIGS. 1 through 3B and 7A-7C was applied using fivepositive experimental subjects between the ages of 11 and 60 sufferingfrom moderate to severe bronchial asthma and/or emphysema based on usualclinical criteria, and having evidence of bronchial hyperresponsiveness.Each of the subjects had experienced no signs of bronchial infection formore than 30 days and continued their usual treatments(bronchodilator±inhaled corticoids±anticholinergics). Measurements wereperformed not less than 4 h after the last administration ofshort-acting β2-agonist inhaler. Eleven healthy control subjects betweenthe ages of 9 and 54 with no known risk factors for pulmonary functiondecompensation were consented and studied. There were 5 acuterespiratory distress events in the experimental cohort resulting inunplanned presentation to a hospital emergency department, as shown intable 610 of FIG. 6A. The control subjects were free of known diseaseexcept for hyperlipidemia and mild hypertension.

The capnographic measurements were performed with aspecially-constructed mainstream-sampling circuit with an NDIRcapnometer (e.g., patient monitor 141) operating at 4.260 μm (responsetime 250 ms; weekly calibration with a reference gas mixture (5% CO02)).The mouthpiece was inserted into the subject's mouth, with a softelastomeric component to engage with the subject's upper and lowerteeth. The lips were coapted to close around the mouthpiece and form acomfortable yet air-tight seal. Each subject breathed normally at a ratebetween 8 and 20 bpm throughout the conduct of the capnometry exam,inhaling air through the nose and exhaling through the check-valvedcapnometry circuit. The subjects watched videos or performed browsing oremail on a laptop computer during a 30-min interval for the capnometrytimeseries acquisition. The analog measurements of fractional expiredcarbon dioxide (feCO2) signal were digitized at 100 Hz sampling rateusing a commercially-available 12-bit analog-to-digital conversionmodule. End-tidal carbon dioxide (etCO2) concentration was ascertainedby determining end-tidal plateau (E3) phase by zero-crossingfirst-derivative calculations from the capnometry time series.

From a methodological point of view, due to physiological irregularitiesof respiration, in some embodiments, it is desirable to select goodquality cycles according to criteria of duration, amplitude and, whenpossible, regularity of the capnography curve. For example, in oneembodiment, respiratory cycles which do not meet the following criteriaare censored out: 1) expiration lasting between 0.8 and 3.0 sec; 2)maximal CO2 concentration above 3.5%; and 3) good breath-to-breathregularity of rapid-rise phase (E2) and end-tidal plateau phase (E3).Additionally, in some outpatient office environments with forced-airheating, the cycling of the furnace may produce minute-to-minutefluctuations of 500 ppm (0.05%; 0.38 mmHg) CO2 or more in exam roomswith high air flux. In such cases, the use of the invention may requireplacement in an area that is well-stirred but not subject to such highair flux or is heated by hot water or electric rather than force-airmeans.

FIGS. 5B and 5A show a portion of the raw data (FIG. 5B) and curves 510and 520 of FIG. 5A, depicting (with reference to the columns of FIG. 5B)raw_t vs. (CO2 and raw_t vs. graph, respectively. (Example program 700describes specific operations carried out on the sampled capnometrysignal to determine the column values of FIG. 5B.)

Continuing with the example embodiment, with reference to FIG. 6A, thedetermined CNS accurately predicted pulmonary function decompensation asshown in items 625 and 650 of FIG. 6A, where P<0.003 Fisher Exact Test,two-tailed. In the study connected with this example embodiment, thesensitivity of the CNS metric to predict pulmonary functiondecompensation was 80% and the specificity was 100%. The lower 95%confidence limit of the odds-ratio was >2.5 and thenumber-needed-to-treat (NNT) was 2. Item 625 of FIG. 6A shows the ROCcurve representing the accuracy and discriminating classificatorycapacity of this example embodiment on the cohort of 16 subjects. (FIG.6B depicts one example of a computer program for determining this ROCcurve.)

Insofar as a only small sample size of cases and controls was available,risk stratification by pulmonary diagnosis or other patient-groupingvariables was not evaluated In this specific example. However it iscontemplated that other embodiments may include specific submodels topredict pulmonary function decompensation in the presence of thosecovariables.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the spiritand scope of the present invention. Embodiments of the present inventionhave been described with the intent to be illustrative rather thanrestrictive. Alternative embodiments will become apparent to thoseskilled in the art that do not depart from its scope. A skilled artisanmay develop alternative means of implementing the aforementionedimprovements without departing from the scope of the present invention.

Accordingly, in one aspect, an embodiment of the invention is directedto a method for automatically predicting acute respiratorydecompensation events that are likely to result in the need for acutemedical attention in humans. The method includes obtaining capnometrysignals representative of capnometry of an individual; detecting thepresence of abnormal timeseries nonlinearity properties of capnometrymeasurements in said signals; determining, utilizing an objectivefunction, a capnometry nonlinearity score (CNS) from said signals basedon one or a plurality of previous time intervals; and determining adifference between a plurality of metrics comprising score and areference value to classify the likelihood of events leading topulmonary function decompensation within a future time interval, whereina significant difference is indicative of an increased risk for acuterespiratory distress. In some embodiments of the method, the objectivefunction evaluates digitized capnometric capnometry timeseries from theone or a plurality of previous time intervals to classify the likelihoodof a cascade of events leading to acute respiratory distress within afuture time interval.

In some embodiments of the method, the objective function comprises aplurality of timeseries nonlinearity metric calculated fromserially-acquired acceleration values acquired at a rate of not lessthan 10 samples per second for a duration not less than 30 minutes.Further, in some of these embodiments of the method, the results of theobjective function are used by a decision-support algorithm to determinea quantitative risk for pulmonary function decompensation. Stillfurther, in some of these embodiments, the decision-support algorithm isan ensemble model that combines nonlinearity measures for respiratoryevents-risk comprising at least 3 different timeseries nonlinearitymeasures.

In some embodiments of the method, the reference value is determinedbased on parameters associated with the individual including at leastone of age, mobility, and pulmonary function decompensation history;wherein the determined difference is determined as significant when thedifference exceeds a threshold; and wherein the threshold is based on avalue indicative of minor fluctuations in activity level of theindividual.

In some embodiments of the method, obtaining capnometry signals involvesnon-dispersive infrared spectrometry or dispersive infrared spectrometryin at least one optical band, and wherein the CO₂ gas possesses a strongabsorptive band, such as bands at infrared wavelengths of 4.260 μm or2.004 μm. Further, in some of these embodiments, the sampling method maybe either via a mainstream circuit or sidestream-sampled capnometrycircuit; the measurement means involves digitization of CO₂ in the range300 ppm to 100,000 ppm, with at least a 12-bit digital precision andaccuracy; the measurement means involves frequent sampling of the feCO₂signal at a sampling rate of at least 10 Hz; the measurement meansinvolves a sensor possessing response time constant of 500 msec or less;and/or the measurement means involves a breathing circuit having acontained volume (“mechanical dead volume”) that is less than the“anatomical dead volume” of large airways (bronchi, trachea, mouth) ofthe human subjects whose respiratory function is to be determined.

In another aspect, an embodiment of the invention is directed to one ormore computer-readable media having computer-executable instructionsembodied thereon that when executed, facilitate a method for determininga capnometry nonlinearity score for an individual. The method includes,identifying capnometry information representative of expired carbondioxide from an individual; and determining a capnometry nonlinearityscore based on the capnometry information from one or a plurality ofprevious time intervals, for determining a likelihood of pulmonaryfunction decompensation within a future time interval.

In yet another aspect, an embodiment of the invention is directed to amethod for automatically predicting acute pulmonary functiondecompensation in humans. The method includes obtaining capnometrysignals representative of capnometry of an individual; determining,utilizing an objective function, a capnometry nonlinearity score (CNS)from said signals based on one or a plurality of previous timeintervals, to classify a likelihood of events leading to pulmonaryfunction decompensation within a future time interval; and determining adifference between the score and a reference value, wherein asignificant difference is indicative of an increased risk for pulmonaryfunction decompensation.

It will be understood that certain features and subcombinations are ofutility and may be employed without reference to other features andsubcombinations and are contemplated within the scope of the claims. Notall steps listed in the various figures need be carried out in thespecific order described. Accordingly, the scope of the invention isintended to be limited only by the following claims.

What is claimed is:
 1. Non-transitory computer-readable media havingcomputer-executable instructions embodied thereon that when executed,facilitate a method for automatically predicting acute respiratorydecompensation events that are likely to result in the need for acutemedical attention in humans, the method comprising: obtaining capnometrysignals representative of capnometry for an individual; detecting thepresence of abnormal timeseries nonlinearity properties of capnometrymeasurements in said signals; generating a capnometry nonlinearity score(CNS) from said signals based on one or a plurality of previous timeintervals; determining a difference between the CNS and a referencevalue to quantify the likelihood of pulmonary function decompensationwithin a future time interval; and based on the determined differenceexceeding a threshold, emitting an electronic notification indicative ofan increased risk for acute respiratory distress for the individual. 2.The computer-readable media of claim 1, wherein generating the CNS isbased on an objective function evaluation of digitized capnometriccapnometry timeseries from the one or a plurality of previous timeintervals to classify the likelihood of a cascade of events leading toacute respiratory distress within a future time interval.
 3. Thecomputer-readable media of claim 1, wherein the capnometry signalscomprise serially-acquired acceleration values acquired at a rate of notless than 10 samples per second for a duration not less than 30 minutes.4. The computer-readable media of claim 3, wherein the CNS is based onan ensemble model comprising nonlinearity measures for at least 3 setsof the capnometry signals.
 5. The computer-readable media of claim 1,wherein the reference value is determined based on parameters associatedwith the individual including at least one of age, mobility, andpulmonary function decompensation history.
 6. The computer-readablemedia of claim 1, wherein the capnometry signals are based onnon-dispersive infrared spectrometry or dispersive infrared spectrometryin at least one optical band.
 7. The computer-readable media of claim 6,where the at least one optical band comprises wavelengths of 4.260 um or2.004 um.